{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping the tracks with water levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook show the way to map tracks with the water level information.\n",
    "\n",
    "1. Mapping one track\n",
    "2. Mapping multiple tracks\n",
    "\n",
    "In later notebook, the temporal water level changes would be dealt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Required setting in the previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import h5py\n",
    "import re\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### load files\n",
    "## set the directory\n",
    "data_home = Path('/home/jovyan/ICESat_water_level/extraction/download/')\n",
    "## list them up and check them\n",
    "files= list(data_home.glob('*.h5'))\n",
    "## choose a file as an example\n",
    "file_latest = files[37]\n",
    "\n",
    "##### function of reading alt13\n",
    "def alt13_to_df(filename, beam):    \n",
    "    f = h5py.File(filename, 'r')\n",
    "    f_beam = f[beam]\n",
    "    lat = f_beam['segment_lat'][:]\n",
    "    long = f_beam['segment_lon'][:]\n",
    "    ws = f_beam['ht_water_surf'][:]\n",
    "    ws_sd = f_beam['stdev_water_surf'][:]\n",
    "    ws_err = f_beam['err_ht_water_surf'][:]\n",
    "    ortho = f_beam['ht_ortho'][:]\n",
    "    wd = f_beam['water_depth'][:]\n",
    "    alt13_df = pd.DataFrame({'Latitude':lat,'Longitude':long,'SurfaceH':ws,\n",
    "                            'SH_SD':ws_sd, 'SH_error':ws_err,'OrthoH':ortho,\n",
    "                            'WaterD':wd})\n",
    "    return alt13_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check whether it works well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Latitude   Longitude   SurfaceH  SH_SD      SH_error    OrthoH  \\\n",
      "0    12.572414  104.233827 -15.095439  0.030  3.402823e+38  2.840535   \n",
      "1    12.572625  104.233804 -15.091765  0.030  3.402823e+38  2.844710   \n",
      "2    12.572881  104.233778 -15.077269  0.030  3.402823e+38  2.860193   \n",
      "3    12.573246  104.233740 -15.074327  0.030  3.402823e+38  2.864084   \n",
      "4    12.574486  104.233615 -15.103493  0.030  3.402823e+38  2.838326   \n",
      "..         ...         ...        ...    ...           ...       ...   \n",
      "337  12.952379  104.195111 -15.540386  0.025  3.402823e+38  3.131171   \n",
      "338  12.952545  104.195094 -15.568451  0.025  3.402823e+38  3.103435   \n",
      "339  12.953128  104.195033 -15.562346  0.025  3.402823e+38  3.110859   \n",
      "340  12.954445  104.194900 -15.566502  0.025  3.402823e+38  3.109011   \n",
      "341  12.954828  104.194861 -15.532722  0.025  3.402823e+38  3.143470   \n",
      "\n",
      "           WaterD  \n",
      "0    3.402823e+38  \n",
      "1    3.402823e+38  \n",
      "2    3.402823e+38  \n",
      "3    3.402823e+38  \n",
      "4    3.402823e+38  \n",
      "..            ...  \n",
      "337  3.402823e+38  \n",
      "338  3.402823e+38  \n",
      "339  3.402823e+38  \n",
      "340  3.402823e+38  \n",
      "341  3.402823e+38  \n",
      "\n",
      "[342 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "gt2r = alt13_to_df(file_latest,'gt2r')\n",
    "print(gt2r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Mapping one track with water level information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use `surface water hegiht` and `Orthometric height` as water level infromation.\n",
    "\n",
    "### Showing the water level information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface water height - Min. height: -16.821245 (m) Max. height: -14.976893 (m)\n",
      "Orthometric height - Min. height: 1.7828771 (m) Max. height: 3.4429538 (m)\n"
     ]
    }
   ],
   "source": [
    "print('Surface water height - Min. height:', gt2r['SurfaceH'].min(),'(m)', 'Max. height:', gt2r['SurfaceH'].max(),'(m)')\n",
    "print('Orthometric height - Min. height:', gt2r['OrthoH'].min(),'(m)', 'Max. height:', gt2r['OrthoH'].max(),'(m)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl        \n",
    "import cartopy.io.img_tiles as cimgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### bounds of Tonle Sap Lake\n",
    "sp_ex = [103.643, 104.667, 12.375, 13.287]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## function for mapping the water level \n",
    "def ic2_map_wl(file, beam, bound, water_levels):\n",
    "    ic2 = alt13_to_df(file,beam)\n",
    "    fig = plt.figure(figsize=(9,10))\n",
    "    ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "    ax.set_extent(bound, crs=ccrs.PlateCarree())\n",
    "    plt.scatter(ic2['Longitude'], ic2['Latitude'], s=10, c=ic2[water_levels], alpha=.7, transform=ccrs.PlateCarree(), cmap='terrain')\n",
    "    plt.colorbar(fraction=0.0320, pad=0.02, label='Elevation (m)')\n",
    "\n",
    "    ##load Google Sat.Map\n",
    "    #request = cimgt.GoogleTiles(style='satellite')\n",
    "    request = cimgt.Stamen('terrain-background')\n",
    "    ax.add_image(request, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic2_map_wl(file_latest,'gt2r',sp_ex,'OrthoH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic2_map_wl(file_latest,'gt2r',sp_ex,'SurfaceH')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing water level for all beams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## function for mapping the water level from every beam\n",
    "def ic2_map_wl_beams(file, bound, water_levels):\n",
    "    fig = plt.figure(figsize=(9,10))\n",
    "    ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "    ax.set_extent(bound, crs=ccrs.PlateCarree())\n",
    "    beam = ['gt1l','gt1r','gt2l','gt2r','gt3l','gt3r']\n",
    "    for i in beam:\n",
    "        ic2 = alt13_to_df(file,i)\n",
    "        plt.scatter(ic2['Longitude'], ic2['Latitude'], s=10, c=ic2[water_levels], alpha=.7, transform=ccrs.PlateCarree(), cmap='terrain')\n",
    "    plt.colorbar(fraction=0.0320, pad=0.02, label='Elevation (m)')\n",
    "\n",
    "    ##load Google Sat.Map\n",
    "    #request = cimgt.GoogleTiles(style='satellite')\n",
    "    request = cimgt.Stamen('terrain-background')\n",
    "    ax.add_image(request, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic2_map_wl_beams(file_latest,sp_ex,'OrthoH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic2_map_wl_beams(file_latest,sp_ex,'SurfaceH')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Mapping multiple tracks with water level information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping the tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the multiple repeat track around Tonle Sap Lake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## list\n",
    "# print(files)\n",
    "\n",
    "D_dict={}\n",
    "error_count=0\n",
    "for ff in files:\n",
    "    try:\n",
    "        D_dict[ff]=alt13_to_df(ff, 'gt2l')\n",
    "    except KeyError as e:\n",
    "        print(f'file {ff} encountered error {e}')\n",
    "        error_count += 1\n",
    "print(f\"read {len(D_dict)} data files of which {error_count} gave errors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 37 files with 'gt2l' among 38 files. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(9,10))\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent(sp_ex, crs=ccrs.PlateCarree())\n",
    "for index,Di in D_dict.items():\n",
    "    plt.scatter(Di['Longitude'], Di['Latitude'],s=2)\n",
    "\n",
    "request = cimgt.Stamen('terrain-background')\n",
    "ax.add_image(request, 10)\n",
    "plt.title(\"Tracks of gt2l on TSL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mapping all tracks with all beams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### new dictionaries for collecting all beams in files\n",
    "D_dict_beams={}\n",
    "error_count=0\n",
    "beam = ['gt1l','gt1r','gt2l','gt2r','gt3l','gt3r']\n",
    "for fff in files:\n",
    "    for bb in beam:\n",
    "        try:\n",
    "            D_dict_beams[str(fff)+'_'+str(bb)]=alt13_to_df(fff, bb)\n",
    "        except KeyError as e:\n",
    "            print(f'file {fff} encountered error {e}')\n",
    "            error_count += 1\n",
    "print(f\"read {len(D_dict_beams)} data files of which {error_count} gave errors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(9,10))\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent(sp_ex, crs=ccrs.PlateCarree())\n",
    "for index,Di in D_dict_beams.items():\n",
    "    plt.scatter(Di['Longitude'], Di['Latitude'],s=2)\n",
    "\n",
    "request = cimgt.Stamen('terrain-background')\n",
    "ax.add_image(request, 10)\n",
    "plt.title(\"Tracks on TSL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping with water levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a function for mapping water level of all ICESat-2 on the TSL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ic2_map_wl_beams_all(dic, bound, water_levels,title):\n",
    "    fig = plt.figure(figsize=(9,10))\n",
    "    ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "    ax.set_extent(bound, crs=ccrs.PlateCarree())\n",
    "    for index,Di in dic.items():\n",
    "        plt.scatter(Di['Longitude'], Di['Latitude'],s=2,c=Di[water_levels], alpha=.7)\n",
    "   \n",
    "    request = cimgt.Stamen('terrain-background')\n",
    "    ax.add_image(request, 10)\n",
    "    plt.title(title)\n",
    "    plt.colorbar(fraction=0.0320, pad=0.02, label='Elevation (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### water levels = Surface water height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic2_map_wl_beams_all(D_dict,sp_ex,'SurfaceH',\"Tracks of gt2l on TSL: Surface water height\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See water levels of all beams of all tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic2_map_wl_beams_all(D_dict_beams,sp_ex,'SurfaceH',\"All tracks of all beams on TSL: Surface water height\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### water levels = SOrthometric height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic2_map_wl_beams_all(D_dict,sp_ex,'OrthoH',\"Tracks of gt2l on TSL: Orthometric height\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic2_map_wl_beams_all(D_dict_beams,sp_ex,'OrthoH',\"Tracks of gt2l on TSL: Orthometric height\")"
   ]
  }
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